Evaluating a blended degree program through the use of the NSSE framework
Bibliographic record
Abstract
Abstract The purpose of this student‐faculty partnership research study was to evaluate the effectiveness of a blended four‐year Bachelor of Education Elementary Program at a Canadian university using the National Survey of Student Engagement (NSSE) framework. Data was collected from the first graduating cohort of students from the B.Ed. program in partnership with four Undergraduate Student Research Assistants (USRA). The students in this study completed online surveys and participated in focus groups at the end of their first and fourth years in the program. The study participants provided recommendations for improving the quality of the program based on the five NSSE benchmarks and the use of digital technologies. The main recommendations that emerged from this study were that student and faculty interactions, outside of the classroom, could be enhanced through the use of web‐based conferencing tools to support “virtual” office hours. Course assignments that incorporate peer mentoring activities through the use of social media applications could provide richer opportunities for active and collaborative learning. Creating more intentional connections between academic coursework and field placements through the use of Google applications could help to strengthen the relationship between theory and practice in the program. Enriching educational experiences could be expanded through the use of social media applications to promote and communicate student led academic and social events. A supportive campus environment could be improved by the development of a digital “road map” and co‐curricular record for the program.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".